Modelling control of epidemics spreading by long-range interactions

2009
journal article
article
40
cris.lastimport.wos2024-04-09T20:40:49Z
dc.abstract.enWe have studied the spread of epidemics characterized by a mixture of local and non-local interactions. The infection spreads on a two-dimensional lattice with the fixed nearest neighbour connections. In addition, long-range dynamical links are formed by moving agents (vectors). Vectors perform random walks, with step length distributed according to a thick-tail distribution. Two distributions are considered in this paper, an alpha-stable distribution describing self-similar vector movement, yet characterized by an infinite variance and an exponential power characterized by a large but infinite variance. Such long-range interactions are hard to track and make control of epidemics very difficult. We also allowed for cryptic infection, whereby an infected individual on the lattice can be infectious prior to showing any symptoms of infection or disease. To account for such cryptic spread, we considered a control strategy in which not only detected, i.e. symptomatic, individuals but also all individuals within a certain control neighbourhood are treated upon the detection of disease. We show that it is possible to eradicate the disease by using such purely local control measures, even in the presence of long-range jumps. In particular, we show that the success of local control and the choice of the optimal strategy depend in a non-trivial way on the dispersal patterns of the vectors. By characterizing these patterns using the stability index of the alpha-stable distribution to change the power-law behaviour or the exponent characterizing the decay of an exponential power distribution, we show that infection can be successfully contained using relatively small control neighbourhoods for two limiting cases for long-distance dispersal and for vectors that are much more limited in their dispersal range.pl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki im. Mariana Smoluchowskiegopl
dc.contributor.authorDybiec, Bartłomiej - 102110 pl
dc.contributor.authorKleczkowski, Adampl
dc.contributor.authorGilligan, Christopher A.pl
dc.date.accessioned2018-04-13T08:14:36Z
dc.date.available2018-04-13T08:14:36Z
dc.date.issued2009pl
dc.description.number39pl
dc.description.physical941-950pl
dc.description.publication1,2pl
dc.description.volume6pl
dc.identifier.doi10.1098/rsif.2008.0468pl
dc.identifier.eissn1742-5662pl
dc.identifier.issn1742-5689pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/53365
dc.languageengpl
dc.language.containerengpl
dc.rightsDodaję tylko opis bibliograficzny*
dc.rights.licenceBez licencji otwartego dostępu
dc.rights.uri*
dc.subject.enepidemiological modellingpl
dc.subject.endisease spreadpl
dc.subject.enstochastic modellingpl
dc.subject.enepidemiological controlpl
dc.subject.endispersal patternspl
dc.subtypeArticlepl
dc.titleModelling control of epidemics spreading by long-range interactionspl
dc.title.journalJournal of the Royal Society Interfacepl
dc.typeJournalArticlepl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-09T20:40:49Z
dc.abstract.enpl
We have studied the spread of epidemics characterized by a mixture of local and non-local interactions. The infection spreads on a two-dimensional lattice with the fixed nearest neighbour connections. In addition, long-range dynamical links are formed by moving agents (vectors). Vectors perform random walks, with step length distributed according to a thick-tail distribution. Two distributions are considered in this paper, an alpha-stable distribution describing self-similar vector movement, yet characterized by an infinite variance and an exponential power characterized by a large but infinite variance. Such long-range interactions are hard to track and make control of epidemics very difficult. We also allowed for cryptic infection, whereby an infected individual on the lattice can be infectious prior to showing any symptoms of infection or disease. To account for such cryptic spread, we considered a control strategy in which not only detected, i.e. symptomatic, individuals but also all individuals within a certain control neighbourhood are treated upon the detection of disease. We show that it is possible to eradicate the disease by using such purely local control measures, even in the presence of long-range jumps. In particular, we show that the success of local control and the choice of the optimal strategy depend in a non-trivial way on the dispersal patterns of the vectors. By characterizing these patterns using the stability index of the alpha-stable distribution to change the power-law behaviour or the exponent characterizing the decay of an exponential power distribution, we show that infection can be successfully contained using relatively small control neighbourhoods for two limiting cases for long-distance dispersal and for vectors that are much more limited in their dispersal range.
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki im. Mariana Smoluchowskiego
dc.contributor.authorpl
Dybiec, Bartłomiej - 102110
dc.contributor.authorpl
Kleczkowski, Adam
dc.contributor.authorpl
Gilligan, Christopher A.
dc.date.accessioned
2018-04-13T08:14:36Z
dc.date.available
2018-04-13T08:14:36Z
dc.date.issuedpl
2009
dc.description.numberpl
39
dc.description.physicalpl
941-950
dc.description.publicationpl
1,2
dc.description.volumepl
6
dc.identifier.doipl
10.1098/rsif.2008.0468
dc.identifier.eissnpl
1742-5662
dc.identifier.issnpl
1742-5689
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/53365
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Dodaję tylko opis bibliograficzny
dc.rights.licence
Bez licencji otwartego dostępu
dc.rights.uri*
dc.subject.enpl
epidemiological modelling
dc.subject.enpl
disease spread
dc.subject.enpl
stochastic modelling
dc.subject.enpl
epidemiological control
dc.subject.enpl
dispersal patterns
dc.subtypepl
Article
dc.titlepl
Modelling control of epidemics spreading by long-range interactions
dc.title.journalpl
Journal of the Royal Society Interface
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

* The migration of download and view statistics prior to the date of April 8, 2024 is in progress.

Views
1
Views per month

No access

No Thumbnail Available